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A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems

In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge econo...

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Autores principales: Zhang, Yang, Chen, Yourong, Miao, Kelei, Ren, Tiaojuan, Yang, Changchun, Han, Meng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739052/
https://www.ncbi.nlm.nih.gov/pubmed/36501830
http://dx.doi.org/10.3390/s22239125
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author Zhang, Yang
Chen, Yourong
Miao, Kelei
Ren, Tiaojuan
Yang, Changchun
Han, Meng
author_facet Zhang, Yang
Chen, Yourong
Miao, Kelei
Ren, Tiaojuan
Yang, Changchun
Han, Meng
author_sort Zhang, Yang
collection PubMed
description In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system.
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spelling pubmed-97390522022-12-11 A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems Zhang, Yang Chen, Yourong Miao, Kelei Ren, Tiaojuan Yang, Changchun Han, Meng Sensors (Basel) Article In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system. MDPI 2022-11-24 /pmc/articles/PMC9739052/ /pubmed/36501830 http://dx.doi.org/10.3390/s22239125 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Yang
Chen, Yourong
Miao, Kelei
Ren, Tiaojuan
Yang, Changchun
Han, Meng
A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title_full A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title_fullStr A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title_full_unstemmed A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title_short A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems
title_sort novel data-driven evaluation framework for fork after withholding attack in blockchain systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739052/
https://www.ncbi.nlm.nih.gov/pubmed/36501830
http://dx.doi.org/10.3390/s22239125
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